Graph analysis is an important type of data analytics where the underlying data-set is modeled as a graph. Since such a graph representation captures relationships between data entities, applying graph analysis procedures can provide valuable insight about the original data-set to the user. Examples of popular graph analysis procedures are Community Detection, PageRank, Shortest Path Finding, and Link Prediction.
Two different types of systems have emerged for graph processing. One type is a graph database that manages graph data in persistent storage. The other type is graph analytic framework that enables fast computation on graph data. A graph analytic framework adopts in-memory computation, because out-of-core computation on graph data is significantly slower than in-memory computation.
Therefore, in the second type of system, graph data “migration” becomes an important step in graph data processing. Graph data migration refers to the process of moving graph data from the database into a graph analytic framework for data analysis. In the case of large graph data sets, graph data migration may take a significant amount of time.
One approach for graph data migration is graph data being exported from a database to a file system and then being imported from the file system into a graph analytic framework. However, such a file-based graph data migration takes a significant amount of time and is not user friendly.
The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.